Managing migration of an application from a source to a target

ABSTRACT

Aspects of the disclosure relate to managing migration of an application. The managing migration of an application includes establishing a source dataset. The source dataset includes a set of source features. The source features relate to a source. The source includes the application. A determination of a first set of migration plans is made. The determination is made with an evaluation. The evaluation is made using the source dataset and a set of legacy features. The evaluation is performed with a cost measure. The application is migrated from the source to the target. The migration is based on the determined first set of migration plans.

BACKGROUND

The present disclosure relates to computer systems, and morespecifically, to managing a migration of an application from a source toa target.

In modern network systems, applications are hosted on a set of computersystems and accessed on a set of networks provided by the set ofcomputer systems. Occasions exist where the set of computer systems orthe set of networks may change from a first location to a secondlocation. If the set of computer systems or the set of networks change,an application may be migrated from the first location to the secondlocation. Different methods for migrating the application can be used.

SUMMARY

Aspects of the disclosure relate to managing migration of an applicationfrom a source to a target. Aspects of the disclosure includeestablishing a source dataset. The source dataset includes a set ofsource features. The source features relate to a source. The sourceincludes the application. A determination of a first set of migrationplans is made. The determination is made using an evaluation. Theevaluation is made using the source dataset and a set of legacyfeatures. The evaluation is performed with a cost measure. Theapplication is migrated from the source to the target. The migration isbased on the determined first set of migration plans.

Aspects of the disclosure may include a learning process. The learningprocess may improve the migration plan determination through migrations.Aspects of the disclosure may include a comparison of the source datasetto the set of legacy features. The set of legacy features may includesource datasets of previously migrated applications. The comparison maydetermine the set of migration plans. Embodiments may include thedetermination of the set of migration plans employing a cost measure.Aspects of the disclosure may include a stability test of the migration.The stability test may include numerous probabilistic extrapolations ofthe application onto the target per the migration plan. Alternatively,in an embodiment the user may guide aspects of the disclosure to createa new migration plan. Aspects of the disclosure may present determinedmigration plans to aid in the creation of the new migration plan.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings included in the present application are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 2 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 3 depicts abstraction model layers according to an embodiment ofthe present invention.

FIG. 4 depicts a method of managing migration of an applicationaccording to an embodiment.

FIG. 5 depicts a flowchart of managing migration of an applicationaccording to an embodiment.

FIG. 6 depicts a system of managing migration of an applicationaccording to an embodiment.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

DETAILED DESCRIPTION

Aspects of the disclosure can assist in migrating a set of softwaremodules (e.g., applications) to a target computer networking environmentfrom a source computing environment. The source may have attributesincluding source memory requirements, source disk space requirements,source use definition, and middleware-to-middleware topology. Adiscovery process may establish a set of attributes (e.g., sourcefeatures). Aspects of the disclosure may compare the source features anda set of legacy features. The set of legacy features may be associatedwith a previously migrated application (e.g., legacy application), andin some embodiments can include patterns. Patterns can contain dataregarding legacy features and previously migrated applications. Thecomparison can use a similarity process to identify a legacy applicationwith a cost measure better than a threshold. A distribution function canmigrate the application to the target in a manner consistent with themigration of legacy applications.

Aspects of the disclosure may include using a learning process for thecomparison. The learning process can include a set of operations. In anembodiment an operation may include capturing a profile of theapplication. The profile may be a format, such as an n-dimensionalfeature vector. The disclosure may include a stability operation todetermine stability of the post-migration performance of theapplication. Stability may be related to a specific migration plan, andmay help determine the viability of the specific migration plan. Thestability may be relative to a post-migration performance of theapplication related to another specific migration plan. The stabilityoperation may include a series of probabilistic extrapolations of theapplication to the target to determine a stability score. An embodimentof the disclosure may rank a set of possible target definitions basedupon similarity and stability scores. A user may create another targetdefinition from the set of possible target definitions.

Aspects of the disclosure generate a set of migration plans forapplications migrating from a source to a target. When source and targetresources change, users may redefine how applications are hosted. Ifsource and target resources change frequently, finding migration planscan provide challenges for users. The target may include a specificconfiguration on a computer networking environment. In some embodiments,the computer networking environment may be a cloud. The source caninclude a specific computing environment which hosted the applicationprior to migration. Data from a set of previously migrated applicationsmay be clustered and mined to create a set of migration plans. The setof migration plans may include a set of multi-variable configurations ofa set of virtual machines to host the application on the cloud. The setof migration plans can include the configurations employed by the set ofpreviously migrated applications. Aspects of the disclosure may includecomparing the application to the set of previously migrated applicationsto identify migration plans which may be repeatable. Resources of one ormore parties may be saved by expediting the migration process whilehighlighting more efficient options.

Aspects of the disclosure include a computer-implemented method, system,and computer-program product for managing migration of an applicationfrom a source computing environment to a target computing environment.The method, system, and computer program product may work on a number ofoperating systems. Migration management can include a plurality ofoperations for computer-implementation. A source dataset is established.The source dataset may have a set of source features. The set of sourcefeatures may include a source. The source can include an individualcomputer environment which includes the application. Aspects of thedisclosure may compare the source dataset and a set of legacy features.Aspects of the disclosure may determine a set of migration plans. Thecomparison may find a subset of legacy features which passes asimilarity threshold using a cost measure. The set of legacy featuresmay include datasets of previously migrated applications. Theapplication may migrate from the source to the target by making use ofthe set of migration plans.

In embodiments, migration of the application may be requested. Aspectsof the disclosure may include a learning process to improve themigration plan determination through additional migration plandeterminations. The learning process may include establishing aspecifically formatted source dataset for the application. The formattedsource dataset may include future performance data and a set ofcomponents of the source such as the topology of the source, anumber-of-transactions factor of the application, a system-responsefactor of the application, memory usage of the application, disk usageof the application, network usage of the application, or applicationcentral processing unit (CPU) usage. Aspects of the disclosure mayinclude a comparison of the formatted source dataset to a set ofsimilarly formatted source datasets of previously migrated applications.The comparison may use a technique known as a support vector machine(SVM). The comparison may determine a set of migration plans. A user mayselect migration plans. Embodiments may include the determinationemploying a cost measure. The cost measure may include a cost ofengaging the application, a cost of accessing components leveraged bythe application, a speed of the application, or a response time of theapplication. Alternatively, in an embodiment the user may guide the SVMto create a new migration plan. An embodiment may update the set ofsimilarly formatted source datasets to include the source dataset of theapplication.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM® zSeries® systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM pSeries® systems; IBMxSeries® systems; IBM BladeCenter® systems; storage devices; networksand networking components. Examples of software components includenetwork application server software, in one example IBM Web Sphere®application server software; and database software, in one example IBMDB2® database software. (IBM, zSeries, pSeries, Series, BladeCenter,WebSphere, and DB2 are trademarks of International Business MachinesCorporation registered in many jurisdictions worldwide).

Virtualization layer 62 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 64 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 66 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and application migration management. The cloud computingenvironment may be responsible for triggering applications migration forregular maintenance. Data for migration management may be collected bythe cloud computing environment. The cloud computing environment mayanalyze at least a portion of data collected to manage migration.

FIG. 4 is a flowchart illustrating a method 400 for managing migrationof an application from a source to a target. Aspects of method 400 maywork on a number of operating systems. The method 400 begins at block401. The source is the computing environment which hosts the applicationprior to migration. The target is the computing environment to which theapplication may be migrated to. In embodiments, the target computingenvironment is a cloud. Aspects of the method 400 may begin by anend-user requesting migration of the application to the target in thecloud. Alternatively, aspects of the method 400 may begin by asystem-user requesting migration of the application to the target in thecloud.

At block 410, a source dataset is established. The source datasetincludes a set of source features of a source. The source includes thecomputer networking environment which hosts the application prior tomigration. In an embodiment the source may be a single computationaldevice. The source includes the application to be migrated. The sourcefeatures may include topology of the source. Topology may be therelationship between at least two features of network architecture. Thetopology of the source may be configured to describe at least two of agroup consisting of a first physical component of the source (e.g., afirst rack, switch, router, etc.), a second physical component of thesource (e.g., a second rack, switch, router, etc.), a first virtualcomponent of the source (e.g., a first virtual machine), or a secondvirtual component of the source (e.g., a second virtual machine). Thesource features may further include a set of components of the sourcesuch as a number-of-transactions factor (e.g., calculations in a second,transfers in a day, etc.), a system response factor (e.g., time betweencall and response), memory usage, disk usage, network usage, andapplication central processing unit usage.

The source features may be established. In an embodiment, aspects of themethod 400 receive a semantic graph of properties and relationships ofthe source as an input. An end-user may upload characteristics andrequirements to establish the source dataset. An embodiment may alsoallow a system-user to dynamically collect data to establish the sourcedataset to have positive impacts (e.g., resource such as time or money).For example, a system-user may have a script which the system-user runsupon the method beginning 401. The script may pull data from the sourcewithout input from an end-user. Establishing source features withoutintervention from an end-user may reduce time and a source of error.

Aspects of the method 400 may also identify a smaller number and type ofsource features required within the source dataset. For example, aspectsof the disclosure may query migration plans to determine a thresholdnumber of legacy features used in a successful migration. Aspects of themethod 400 may identify the threshold number of legacy features. Themethod may also identify the type (e.g., a transaction factor, CPUusage, memory usage, etc.) of the threshold number of legacy features.Aspects of the disclosure may then gather the number and type of sourcefeatures identified in the threshold number when establishing the sourcedataset. For example, if a previous migration had a set of legacyfeatures consisting of memory usage, disk usage, and a system responsefactor, aspects of the method 400 may create the source dataset with thesame three source features. Finding a smaller number of type of sourcefeatures required may reduce time and cost by eliminating resourceswhich would gather extraneous source features.

At block 420 a first set of migration plans is determined. The first setof migration plans includes a set of configurations for the applicationat the target. The target includes a computer networking environment(e.g., a storage area network, a wide area network, a cloud, etc.) whichthe application may migrate to. The set of configurations includes a setof blades which may host the application, a set of racks which may holdand connect the blades, a set of routers which may allow the applicationto communicate, a set of switches which manage the data pathways of theapplication, and a set of virtual machines which the application mayreside on. The first set of migration plan may be the set ofconfigurations employed by a previously migrated application.

The source dataset and a set of legacy features may undergo anevaluation. The evaluation may include a similarity comparison. The setof legacy features includes a set of source features of previouslymigrated applications. The set of previously migrated applications mayhave been migrated to a different computer networking environment thanthe computer networking environment the application is being migratedto. The set of legacy features are associated with a set of migrationplans. For an example similarity comparison, an example source datasetregarding an application executing 8,000 transactions in a day may havea single virtual machine with three blades in a single rack with asingle router and switch handing data flow. An example set of legacyfeatures regarding an application executing 5,000,000 transactions a daymay have two virtual machines with 250 blades over ten racks with 2routers and 3 switches handling data flow. A similarity comparisonbetween the example source dataset and the example set of legacyfeatures may find the two very dissimilar due to the orders of magnitudeof difference in features.

The evaluation may determine the first set of migration plans using acost measure. A cost measure may include a value factor of engaging theapplication at the target as specified in a migration plan (e.g., a costof using network memory), a value factor of accessing componentsleveraged by the application as specified in a migration plan (e.g., acost of using the racks/routers/switches/etc. allocated by the migrationplan), a performance factor of the application as specified in amigration plan (e.g., calculations in a second, transfers in a day), anda response factor of the application as specified in a migration plan(e.g., time between call and response). The performance factor orresponse factor of a cost measure may be a different value from theperformance factor or response factor of the source features. Thedifferent value may allow a user to make an application behavedifferently post-migration. For example, a supply chain managementapplication may have been able to manage the data of 500 warehousesbefore performance suffered. A user may be able to specify that thesupply chain management application be able to manage the data of 1000warehouses after migration. In this way a user can use aspects of thedisclosure to modify application performance.

The similarity comparison may determine which of the set of legacyfeatures meets a similarity threshold to the source dataset whilemeeting a cost measure threshold. Any of the set of legacy featureswhich meets the similarity threshold and meets the cost measurethreshold may have the migration plan associated with that set of legacyfeatures presented to the user. Alternatively, the similarity comparisonmay present those migration plans which have associated legacy featureswith higher combined similarity scores and cost measure scores. Thepresented migration plans may have the similarity and cost measureresults displayed to the user.

In an embodiment the evaluation may include a learning algorithm. Thelearning algorithm may use a technique known as a support vectormachine. If the method 400 employs a learning algorithm the sourcedataset and the set of legacy features may be specifically formattedbefore the evaluation. The learning algorithm may allow a user to createa new migration plan. The user may create the new migration plan bymodifying the set of migration plans presented by aspects of the method400. The user may alternatively create the new migration plan withoutinput.

At block 430 the application is migrated. The migration may be completedwith any migration plan selected or created by a user. The migration mayinclude installing and configuring components in a manner consistentwith a chosen migration plan. The migration may include creating orconfiguring virtual machines in the manner consistent with the chosenmigration plan. After migration, the source dataset may be added to thesource of legacy features. After migration, any created migration plansmay be added to the set of migration plans.

The method of FIG. 4 may be embodied in multiple ways. An embodiment mayhave an end-user request a migration to bring an application from afirst cloud to a second cloud. Aspects of the disclosure may collect asource dataset regarding how the application was configured at the firstcloud. The source dataset may include the physical components whichsupported the application, the virtual machines which utilizes thephysical components, the performance of the application on the firstcloud, and the required performance of the application on the secondcloud.

Aspects of the disclosure may compare the source dataset to sets of datafrom applications which have been migrated previously. The comparisonmay use cost measure thresholds of the application. The comparison mayfind which sets of data have higher correlations and greater chances ofmeeting the cost measures of the application. Aspects of the disclosuremay find how those previously migrated applications, which had sets ofdata with suitable correlation and cost measure threshold satisfaction,were migrated. The application may then be migrated in the same manneror in substantially the same manner as the previously migratedapplications.

For example, aspects of the disclosure may compare a set of sourcefeatures of a first accounting application to be migrated to a firstcloud to a second, third, and fourth set of legacy features of a second,third, and fourth accounting application which were migrated to thefirst cloud, a second cloud, and a third cloud, respectively. The secondset of legacy features may have a high correlation with the sourcefeatures but a poor chance of meeting the cost measure thresholds of thefirst accounting application. The third set of legacy features may havehad a poor correlation with the source features but a high chance ofmeeting the cost measure thresholds of the first accounting application.The fourth set of legacy features may have both a high correlation withthe source features and a high chance of meeting the cost measurethresholds of the first accounting application. Therefore, the fourthlegacy set may be selected. Aspects of the disclosure may discover howthe fourth accounting application was migrated. The first accountingapplication may then be migrated by a migration plan consistent with howthe fourth accounting application was migrated.

FIG. 5 shows an embodiment of a method 500 for determining a migrationplan. The method 500 may start with a migration request 501 whichtriggers a script for collecting a source dataset of source features503. The formatted source dataset of source features may be established505. In an embodiment, the source dataset would be formatted into asource feature vector. The source feature vector may be n-dimensional.Aspects of the method 500 may pull in a master set 507 of data whichincludes a set of migration plans and an associated set of legacyfeatures. The associated set of legacy features may be formatted into aset of feature vectors. The set of legacy feature vectors may includelegacy feature vectors which are n-dimensional.

Aspects of the embodiment may include a first comparison 550 between thesource feature vector and a legacy feature vector. The method 500 maybegin with the first comparison 550 and may continue with iterativecomparisons 551 until the sets of feature vectors pulled from the masterset 507 have been compared. In FIG. 5 the iterative cycle is illustratedby the arrows in bold. The first comparison may include a set ofoperations. The set of operations may include a similarity test 552, acost measure test 554, a stability test 556, a source datasetdetermination step 558, and a test for more legacy feature vectorswithin the set of feature vectors 560.

In method 500 one operation is the similarity test 552 between thelegacy feature vector and the source feature vector. The similarity test552 may check if the comparison between the legacy feature vector andthe source feature vector meets a threshold number of equivalent ornearly-equivalent features. For example, the source feature vector mayhave a number of blades serving the application. The legacy featurevector may have a same number of blades serving a legacy application.The same number may count as one equivalent feature, raising thesimilarity score and making it more likely to meet the threshold number.However, the example the source feature vector may have a differentapplication CPU speed than the legacy feature vector. The differentapplication CPU speed may count as a non-equivalent feature. Thenon-equivalent feature may lower the similarity score and make thesimilarity test 552 less likely to meet the threshold number. Once asimilarity score is developed, the test may conclude with a pass (yes)or a fail (no), depending upon if the similarity score meets or fails tomeet the similarity threshold, respectively.

The similarity test 552 may be evaluated in a number of ways. In anembodiment, aspects of the method 500 may format the source featurevector as X={X1, X2, X3, . . . Xi, . . . Xn}, where i represents the ithfeature. The method may also have the legacy feature vector as T={T1,T2, T3, . . . Tn}. In the embodiment the similarity score may be theaccumulated deviation from the two feature vectors over normalizedfeatures. The accumulated deviation may be calculated as: D (X,T)=Σ_(i)((x_(i)−T _(i))/σ_(i)), where σ is a weighting coefficient. The resultof the equation may calculate the similarity of any source featurevector.

Another test is the cost measure test 554. Aspects of the method 500 mayutilize the cost measure test 554 if the similarity score passes. Thecost measure test 554 may check if the comparison between the sourcefeature vector and the legacy feature vector meets a threshold number ofequivalent or nearly equivalent cost-measure aspects. For an example, asource feature vector may have a cost measure that the applicationexceed a certain amount of transactions in an hour and the legacyfeature vector has a same legacy cost measure. The same legacy costmeasure may count as a one equivalent feature. The one equivalentfeature may raise the cost measure score of the set of formatted legacyfeatures. The raised cost measure score may make it more likely that thecost measure test may meet the threshold number. However, if the sourcefeature vector has a different required cost for accessing components ata target than the legacy feature vector, the difference may count as anon-equivalent cost-measure. The non-equivalent cost-measure may lowerthe cost measure score and make it less likely to meet the thresholdnumber. The step may conclude with either a cost measure score whichpasses (yes) or fails (no) the cost measure threshold.

The method may test for stability. The stability test 556 may entail theaspects of the disclosure verifying if the migration plan associatedwith the legacy feature vector remains stable over numerousextrapolations. Aspects of the method 500 may test the set of legacyfeature vector with the stability test 556 if the cost measure scorepasses. The stability test 556 may include verifying that the migrationplan meets a stability threshold difference. The stability test 556 mayinclude a set of probabilistic extrapolations of the source featurevector onto the target as specified in the migration plan. The set ofprobabilistic extrapolations may illuminate performance of theapplication at the target. If the variance of the performance meets athreshold difference of equivalent or nearly equivalent performances thestability test 556 may register as a success. For an example, perhaps afirst extrapolation shows that the application executes 4,000transactions in an hour and the second extrapolation shows that theapplication executes 4,150 transactions in an hour. If the thresholddifference is 300 transactions in an hour, the 150 transactions in anhour difference between the first and second extrapolation may count asone equivalent extrapolation, raising the stability score. However, theexample may also have a first extrapolation with a cost of $300 a monthfor network usage at a target and a second extrapolation with a cost of$180 a month for network usage at a target. If the threshold differenceis $50, the $120 difference between the first extrapolation and thesecond extrapolation may count as a non-equivalent extrapolation,lowering the stability score and making it less likely to meet thethreshold difference. The step may conclude with either a stabilityscore which passes (yes) or fails (no) the stability thresholddifference.

If the legacy feature vector has passed all three tests, an associatedmigration plan is determined as suitable for presentation to the user.The associated migration plan is put into a final set of migration plansfor presentation to the user 558. The final set of migration plans mayhave a final comparison score. The final comparisons score may include acomposite score from the three tests to give the user an idea of how theassociated migration plan fared throughout tests.

Aspects of the method 500 may test for another legacy feature vector 560from the set of legacy feature vectors. Aspects of the method 500 maytest for a next legacy feature vector regardless of the result ofprevious tests. If one or more of the comparison test 552, cost measuretest 554, or stability test 556 return failures, the logic of the method500 may skip other tests and conduct the test for a next legacy featurevector 560. The method may check to see if all legacy feature vectorspulled from the set of legacy feature vectors have been compared to thesource feature vector. If there is another legacy feature vector, thetest passes (yes). If all legacy feature vectors have been compared tothe source feature vector, the test returns a negative (no). If the testpasses the next legacy feature vector can be compared to the sourcefeature vector 551 and the operations in the iterative comparison mayrestart.

Once block 560 returns no, the iterative cycle of the method 500 hasended. Aspects of the method 500 may gather the final set of migrationplans. Aspects of the method 500 may then test the final set ofmigration plans with an acceptability test 590. The acceptability testmay include a user check of the migration plan associated with the“passing” set of formatted legacy features. If the user finds themigration plan acceptable, the user may pass (yes) the migration plan.If the user finds the migration plan unacceptable, the user may fail(no) the migration plan.

Any migration plans which have passed may be presented to the user 599as the determined migration plans. If no migration plans have passed,aspects of the disclosure may allow a user to create a new migrationplan 592. In various embodiments, the user must be a qualified user tocreate the new migration plan. In various embodiments, migration plansfrom the master set 507 may be presented for modification to assist increating the new migration plan. The created migration plans may then beused by the user in migrating the application.

Aspects of the method 500 may collect data regarding the source featuresand final migration plan for the master set 507. If a new migration planwas created, the collected data may include both the new migration planand the source feature vector 594. If there was not a new migration plancreated, the source feature vector would be collected 596 and associatedwith the chosen migration plan. The master set 507 would be updated withthe chosen migration plan and source feature vector. The update mayallow aspects of the disclosure to become better at determiningmigration plans with each determination.

The blocks in the method 500 are presented as they are to demonstrate anembodiment of aspects of the disclosure. Many of the blocks may berearranged and still be encompassed within the disclosure. The testswithin the loop specifically may occur in any order. Alternatively, someor all of the tests may occur simultaneously. The tests do not need tohappen in the order detailed in method 500.

FIG. 6 shows embodiments of a system for managing migration of anapplication from a source to a target. In embodiments, method 400 may beimplemented using one or more modules of FIG. 6. These modules may beimplemented in hardware, software, or firmware executable on hardware,or a combination thereof. For example, module functionality that mayoccur work on a host device 695 may actually be implemented in a remotedevice 690 and vice versa. Other functionality may be distributed acrossthe host device 695 and the remote device 690.

The host device 695 may include a managing module 600. The managingmodule may be configured and arranged to manage migrations ofapplications. The managing module may include an establishing module610, a determining module 620, and a migration module 630. Theestablishing module 610 may include an enhancing module 615. Thedetermining module 620 may include an evaluating similarity module 623,an evaluating cost measures module 625, and an evaluating stabilitymodule 627.

The establishing module 610 establishes a source dataset. The sourcedataset includes a set of source features of a source. The sourceincludes the computer networking environment which hosts the applicationprior to migration. The source includes the application to be migrated.The source features may include topology of the source. The establishingmodule 610 may download data for the source dataset or allow upload ofdata for the source dataset.

The establishing module 610 may include an enhancing module 615 forimproving the process of establishing the source dataset. The enhancingmodule 615 may dynamically collect data to establish the source datasetby running scripts to save time and money. The scripts may pull datafrom the source. The enhancing module 615 may identify a smaller numberof source features of a source dataset. To establish the smaller numberof source features, the enhancing module 615 may identify a thresholdnumber of an identified type of source features. The enhancing module615 may determine the threshold number to be the number of legacyfeatures of previously migrated applications.

The determining module 620 may determine a first set of migration plans.The first set of migration plans may govern how the application may behosted after migration. The determining module 620 may determine thefirst set of migration plans with an evaluation. The evaluation may beof the source dataset and a set of legacy features.

The determining module 620 may include an evaluating similarity module623. The evaluating similarity module 623 may evaluate the similaritybetween the set of source features and the set of legacy features. Theevaluating similarity module 623 may determine if the set of legacyfeatures meets a similarity threshold to the set of source features. Ifthe set of legacy features meets the similarity threshold, theevaluating similarity module 623 may identify a migration planassociated with the set of legacy features for the determining module620. The determining module 620 may then determine the migration planfor the user.

The determining module 620 may include an evaluating cost measuresmodule 625. The evaluating cost measures module 625 may evaluate the setof legacy features using a cost measure. The evaluating cost measuresmodule 625 may determine if the set of legacy features meets a costmeasure threshold. If the set of legacy features meets the cost measuresthreshold, the evaluating cost measures module 625 may identify amigration plan associated with the set of legacy features for thedetermining module 620. The determining module 620 may then determinethe migration plan for the user.

The determining module 620 may include an evaluating stability module627. The evaluating stability module 627 may evaluate a projectedstability of application performance at the target per a migration plan.The evaluating stability module 627 may determine projected stability byextrapolating performance of the application at the target with numerousprobabilistic predictions. The numerous probabilistic predictions maydetermine the range of performances of the application at the target.The projected stability may be better with a smaller range ofperformances. The evaluating stability module 627 may determine if theprojected stability meets a stability threshold. If the projectedstability meets the stability threshold, the evaluating stability module627 may identify the migration plan associated with the projectedstability for the determining module 620. The determining module 620 maythen determine the migration plan for the user.

The determining module 620 may determine which of the set of legacyfeatures meets both a similarity threshold and a cost measure thresholdwhile the associated projected stability meets a stability threshold.Any of the set of legacy features which meets the similarity threshold,cost measure threshold, and stability threshold may have the migrationplan associated with that set of legacy features presented to the user.Alternatively, the determining module 620 may identify a set ofmigration plans which have associated legacy features with bettersimilarity scores, better cost measure scores, and better stabilityscores. The determining module 620 may present the identified set ofmigration plans to the user. The determining module 620 may also presentthe similarity scores, cost measures scores, and stability scores of theset of migration plans which was identified to the user. The determiningmodule 620 may allow the user to craft a new migration plan using theidentified set of migration plans. The determining module 620 mayalternatively allow the user to craft a new migration without input.

The migrating module 630 migrates the application. The migrating modulemay migrate the application with any migration plan determined by thedetermining module 620. The migrating module 630 may install componentsin a manner consistent with the chosen migration plan. The migratingmodule 630 may create or configure virtual machines in a mannerconsistent with the chosen migration plan. After migration, themigrating module 630 may add the source dataset to the source of legacyfeatures. After migration, the migrating module 630 may add any createdmigration plans to the set of migration plans. The adding of any createdmigration plans to the set of migration plans may make aspects of thedisclosure more likely to determine an appropriate migration plan infuture determinations.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein. The descriptions of the various embodiments of thepresent disclosure have been presented for purposes of illustration, butare not intended to be exhaustive or limited to the embodimentsdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. The terminology used herein was chosen toexplain the principles of the embodiments, the practical application ortechnical improvement over technologies found in the marketplace, or toenable others of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method for managingmigration of an application, the method comprising: establishing, uponreceiving a request to migrate an application, a source dataset having aset of source features of a source which includes the application;determining for the application, by an evaluation of the source datasetand a set of legacy features, a first set of migration plans that atleast identifies a group of the set of legacy features meeting asimilarity threshold to the source dataset and identifies a portion ofthe set of legacy features meeting a cost measure threshold of thesource dataset; and migrating, based on the first set of migrationplans, the application from the source to a target.
 2. The method ofclaim 1, further comprising: establishing a set of post-migrationperformance data of the application; determining a second set ofmigration plans using the set of post-migration performance data; andestablishing an updated set of legacy features including the set ofsource features of the source.
 3. The method of claim 1, wherein the setof source features includes a source topology of the applicationconfigured to describe at least two features selected from a firstphysical component of the source, a second physical component of thesource, a first virtual component of the source, or a second virtualcomponent of the source.
 4. The method of claim 1, wherein: the sourceincludes a computer networking environment configured to transport theapplication; the target includes a computer networking environmentconfigured to receive the application; the set of source featuresincludes a set of components of the application at the source having anumber-of-transactions factor, a system response factor, memory usage,disk usage, network usage, and application central processing unitusage; the set of legacy features includes a set of source features ofpreviously migrated applications.
 5. The method of claim 1, wherein thecost measure includes at least one feature selected from: a value factorof engaging the application per a migration plan; a value factor ofaccessing components leveraged by the application per a migration plan;a performance factor of the application per a migration plan; and aresponse factor of the application per a migration plan.
 6. The methodof claim 1, wherein the evaluation includes: establishing the sourcedataset as a first n-dimensional feature vectors and the set of legacyfeatures as a set of n-dimensional feature vectors; determining, basedon the first n-dimensional feature vector and the set of n-dimensionalfeature vectors, a set of similarity scores associated with each of theset of n-dimensional feature vectors; and processing, in response to oneor more of the set of n-dimensional feature vectors associated with afirst set of migration plans meeting a similarity score threshold, thefirst set of migration plans.
 7. The method of claim 1, wherein thefirst set of migration plans includes a set of configurations of theapplication at the target used by a previously migrated application, theset of configurations having: a set of racks configured for applicationhosting; a set of routers configured for application communicating; aset of switches configured for application connection managing; and aset of virtual machines configured for application organization.
 8. Themethod of claim 1, wherein the first set of migration plans includes: alegacy target of the previously migrated application in a first computernetworking environment; the target of the application in a secondcomputer networking environment; and a configuration applicable to thefirst computer networking environment, the second computer networkingenvironment, and a third computer networking environment.
 9. The methodof claim 1, wherein establishing the source dataset includes collectingdata via scripts executed by a scripting engine to gather a set ofinputs.
 10. The method of claim 1, further comprising: identifying,using previous migrations, a threshold number of source features used ina successful migration; determining another set of source featuresincluding the threshold number of source features used in the successfulmigration; and establishing a source dataset using the set of sourcefeatures.
 11. The method of claim 1, wherein determining the first setof migration plans includes using a support vector machine.
 12. Themethod of claim 2, wherein establishing the set of post-migrationperformance data of the application further comprises: collecting a setof performance data of the application; and predicting a set ofpost-migration performance data of the application.
 13. A system formanaging migration of an application, the system comprising: a remotedevice; and a host device, wherein at least one of the remote device andthe host device includes a processor, the processor being configured toperform a method comprising: establishing, upon receiving a request tomigrate an application, a source dataset having a set of source featuresof a source which includes the application; determining for theapplication, by an evaluation of the source dataset and a set of legacyfeatures, a first set of migration plans that at least identifies agroup of the set of legacy features meeting a similarity threshold tothe source dataset and identifies a portion of the set of legacyfeatures meeting a cost measure threshold of the source dataset; andmigrating, based on the first set of migration plans, the applicationfrom the source to a target.
 14. The system of claim 13, wherein: theset of source features includes a source topology of the applicationconfigured to describe at least two features selected from a firstphysical component of the source, a second physical component of thesource, a first virtual component of the source, or a second virtualcomponent of the source; the source includes a computer networkingenvironment configured to transport the application; the target includesa computer networking environment configured to receive the application;the set of source features includes a set of components of theapplication at the source having a number-of-transactions factor, asystem response factor, memory usage, disk usage, network usage, andapplication central processing unit usage; the set of legacy featuresincludes a set of source features of previously migrated applications;and the first set of migration plans includes a set of configurations ofthe application at the target used by a previously migrated application,the set of configurations having: a set of racks configured forapplication hosting; a set of routers configured for applicationcommunicating; a set of switches configured for application connectionmanaging; and a set of virtual machines configured for applicationorganization.
 15. The system of claim 13, wherein the method performedby the processor further comprises structuring the source dataset using:scripts to gather a set of inputs; and a threshold number of sourcefeatures used in a successful migration.
 16. The system of claim 13,wherein the method performed by the processor further comprisesidentifying a migration plan, wherein the migration plan has a set ofstability scores of a set of probabilistic extrapolations of theapplication to the target within a stability threshold.
 17. A computerprogram product for managing migration of an application, the computerprogram product disposed upon a computer readable storage medium, thecomputer program product comprising computer program instructions that,when executed by a computer processor of a computer, cause the computerto carry out the steps of: establish, upon receiving a request tomigrate an application, a source dataset having a set of source featuresof a source which includes the application; determine for theapplication, by an evaluation of the source dataset and a set of legacyfeatures, a first set of migration plans that at least identifies agroup of the set of legacy features meeting a similarity threshold tothe source dataset and identifies a portion of the set of legacyfeatures meeting a cost measure threshold of the source dataset; andmigrate, based on the first set of migration plans, the application fromthe source to a target.